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06/24/2023
ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility
Accurate shared micromobility demand predictions are essential for trans...
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06/05/2023
A unified analysis of likelihood-based estimators in the Plackett–Luce model
The Plackett–Luce model is a popular approach for ranking data analysis,...
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04/13/2023
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Real-time forecasting of travel demand during wildfire evacuations is cr...
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03/11/2023
An approximate control variates approach to multifidelity distribution estimation
Forward simulation-based uncertainty quantification that studies the dis...
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02/06/2023
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning
Practical natural language processing (NLP) tasks are commonly long-tail...
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10/25/2022
Wasserstein Archetypal Analysis
Archetypal analysis is an unsupervised machine learning method that summ...
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09/16/2022
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years sugges...
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09/13/2022
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting
Least squares regression is a ubiquitous tool for building emulators (a....
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09/13/2022
Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems
Approximate solutions to large least squares problems can be computed ef...
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07/08/2022
Nonparametric Embeddings of Sparse High-Order Interaction Events
High-order interaction events are common in real-world applications. Lea...
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11/04/2021
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
Sparse representation-based classification (SRC) has attracted much atte...
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11/02/2021
Real-time Forecasting of Dockless Scooter-Sharing Demand: A Context-Aware Spatio-Temporal Multi-Graph Convolutional Network Approach
Real-time demand forecasting for shared micromobility can greatly enhanc...
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09/16/2021
Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data
With increased frequency and intensity due to climate change, wildfires ...
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08/12/2021
Probabilistic methods for approximate archetypal analysis
Archetypal analysis is an unsupervised learning method for exploratory d...
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05/10/2021
Budget-limited distribution learning in multifidelity problems
Multifidelity methods are widely used for statistical estimation of quan...
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03/29/2021
A bandit-learning approach to multifidelity approximation
Multifidelity approximation is an important technique in scientific comp...
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03/11/2021
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
The growing popularity of e-scooters and their rapid expansion across ur...
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02/15/2021
A Global to Local Double Embedding Method for Multi-person Pose Estimation
Multi-person pose estimation is a fundamental and challenging problem to...
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01/11/2021
Randomized weakly admissible meshes
A weakly admissible mesh (WAM) on a continuum real-valued domain is a se...
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01/07/2021
Infections Forecasting and Intervention Effect Evaluation for COVID-19 via a Data-Driven Markov Process and Heterogeneous Simulation
The Coronavirus Disease 2019 (COVID-19) pandemic has caused tremendous a...
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12/22/2020
Open Set Domain Adaptation by Extreme Value Theory
Common domain adaptation techniques assume that the source domain and th...
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12/08/2020
Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels
In model serving, having one fixed model during the entire often life-lo...
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10/19/2020
Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data
Emerging micromobility services (e.g., e-scooters) have a great potentia...
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10/16/2020
Consistency of archetypal analysis
Archetypal analysis is an unsupervised learning method that uses a conve...
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04/13/2020
Analysis of The Ratio of ℓ_1 and ℓ_2 Norms in Compressed Sensing
We first propose a novel criterion that guarantees that an s-sparse sign...
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02/20/2020
A General Pairwise Comparison Model for Extremely Sparse Networks
Statistical inference using pairwise comparison data has been an effecti...
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11/11/2019
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
We study the problem of visual question answering (VQA) in images by exp...
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03/07/2019
Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
Ontology learning is a critical task in industry, dealing with identifyi...
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04/27/2018
k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
k-Nearest Neighbors is one of the most fundamental but effective classif...
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03/10/2018
Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
Sparse representation classification achieves good results by addressing...
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12/26/2017
